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Joint single-cell measurements of nuclear proteins and RNA in vivo

Nature Methods volume 18, pages 1204–1212 (2021)Cite this article

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Abstract

Identifying gene-regulatory targets of nuclear proteins in tissues is a challenge. Here we describe intranuclear cellular indexing of transcriptomes and epitopes (inCITE-seq), a scalable method that measures multiplexed intranuclear protein levels and the transcriptome in parallel across thousands of nuclei, enabling joint analysis of transcription factor (TF) levels and gene expression in vivo. We apply inCITE-seq to characterize cell state-related changes upon pharmacological induction of neuronal activity in the mouse brain. Modeling gene expression as a linear combination of quantitative protein levels revealed genome-wide associations of each TF and recovered known gene targets. TF-associated genes were coexpressed as distinct modules that each reflected positive or negative TF levels, showing that our approach can disentangle relative putative contributions of TFs to gene expression and add interpretability to inferred gene networks. inCITE-seq can illuminate how combinations of nuclear proteins shape gene expression in native tissue contexts, with direct applications to solid or frozen tissues and clinical specimens.

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Fig. 1: inCITE-seq simultaneously measures intranuclear protein and RNA levels at single-nucleus resolution.
Fig. 2: In vivo application of inCITE-seq shows cell type-specific protein expression in the mouse hippocampus.
Fig. 3: inCITE-seq measures changes in nuclear TF levels after stimulation of the mouse hippocampus.
Fig. 4: Inferring TF effects on gene and module expression using joint protein and transcriptome measurements.

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Data availability

Raw gene expression count matrices of all inCITE-seq data, BAM files of mapped reads and the matrix of mouse hippocampus inCITE-seq data jointly embedded with snRNA-seq data are available on Gene Expression Omnibus under the accession GSE163480. Data from Habib et al.54 are available under GSE143758. Data from MULTI-seq used to compare RNA complexity in HEK cells are available under GSE129578. Databases of TF motifs (CIS-BS and JASPAR2018_CORE_vertebrates_non-redundant) are available at http://cisbp.ccbr.utoronto.ca and http://jaspar2018.genereg.net, respectively. Source data are provided with this paper.

Code availability

Code used for analyses is available at https://github.com/klarman-cell-observatory/inCITE-seq.

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Acknowledgements

We thank J. Schmid-Burgk and I. Cheeseman for the HeLa p65–mNeonGreen reporter line, L. Gaffney for assistance with figure preparation, P. Thakore for coining the acronym inCITE-seq, A. Rubin for critical feedback on the manuscript, C. McGinnis for helpful sharing of data, the Broad Institute Flow Cytometry Core facility, and all members of the Regev laboratory for helpful discussions. This research was supported by NIH/NHGRI CEGS grant 5RM1 HG006193. A.R. was a Howard Hughes Medical Institute Investigator (until 31 July 2020). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. Devan Phillips & Aviv Regev

    Present address: Genentech, South San Francisco, CA, USA

  2. These authors contributed equally: Christopher N. Parkhurst, Emma M. Magee.

Authors and Affiliations

  1. Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Hattie Chung, Emma M. Magee, Devan Phillips, Ehsan Habibi, Fei Chen, Julia Waldman & Aviv Regev

  2. Jill Roberts Institute for Research in Inflammatory Bowel Disease, Weill Cornell Medicine, Cornell University, New York, NY, USA

    Christopher N. Parkhurst & David Artis

  3. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA

    Fei Chen

  4. BioLegend Inc, San Diego, CA, USA

    Bertrand Z. Yeung

  5. Friedman Center for Nutrition and Inflammation, Joan and Sanford I. Weill Department of Medicine, Department of Microbiology and Immunology, Weill Cornell Medicine, Cornell University, New York, NY, USA

    David Artis

  6. Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Aviv Regev

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Contributions

H.C. conceived and designed the study with guidance from A.R. C.N.P. designed and performed mouse experiments with guidance from D.A. H.C. and E.M.M. developed and performed inCITE-seq experiments, with buffer optimization input from F.C. and early-stage experimental support from J.W. C.N.P. and E.M.M. conducted immunohistochemistry. D.P. conducted 10x experiments and constructed sequencing libraries. B.Z.Y. conjugated inCITE antibodies. H.C. analyzed and interpreted data with help from E.H. on cis regulatory motif enrichment analysis and supervision from A.R. A.R. provided project oversight and funding. H.C. and A.R. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Hattie Chung or Aviv Regev.

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Competing interests

A.R. is a founder and equity holder of Celsius Therapeutics, an equity holder in Immunitas Therapeutics and, until 31 August 2020, was an SAB member of Syros Pharmaceuticals, Neogene Therapeutics, Asimov and Thermo Fisher Scientific. From 1 August 2020, A.R. is an employee of Genentech. From May 2021, D.P. is an employee of Genentech. B.Z.Y. was formerly an employee of BioLegend and is now an employee of Spatial Genomics. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Methods thanks Dominic Grun, Bing Ren and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Lei Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Optimization of intranuclear antibody staining in HeLa cells.

a. Nuclear p65 levels change after TNFα treatment, while total p65 in cells remains unchanged. Distribution of p65-mNeonGreen reporter fluorescence (x axis; % mode of singlet nuclei, y axis) measured by flow cytometry of nuclei (solid line) vs. cells (dashed line) from untreated (‘NT’, blue) or TNFα treated cells (red). b. Flow cytometry distinguishes p65-mNeonGreen signals across mixtures of NT and TNFα. Top: Flow cytometry measures of mNeonGreenhigh fraction (x axis) match the input fraction of TNFα nuclei (x axis). Bottom: Corresponding high (red) and low (blue) mNeonGreen distributions. c. Immunofluorescence of nuclei smeared onto a slide after intranuclear p65 stain in suspension, showing complete antibody diffusion into the nucleus; representative of 3 experiments. Scale: 100 µm. d,e. Comparing antibody- and fluorescence reporter-derived p65 levels. Antibody (from Alexa Fluor 647 secondary, y axis) and mNeonGreen (x axis) signal of p65 in an equal mixture of NT and TNFα stimulated nuclei. Histograms: marginal distributions. d. Agreement between unconjugated p65 antibody and mNeonGreen signal. e. No relationship between DNA-conjugated p65inCITE-Ab and mNeonGreen signal using standard intranuclear staining buffer (pre-optimization). f. Relation between nuclei hashtag oligonucleotide (HTO; x axis) counts and p65 antibody-derived tag (ADT; y axis) counts, shown across 10,014 NT and TNFα nuclei, colored by the number of RNA UMIs. Top left: Pearson R2 and associated P-value (two-sided t-test). To control for this relation, we normalize protein ADT counts by nuclei HTO counts (Methods). g. Comparing RNA complexity from inCITE-seq (fixed HeLa nuclei) and MULTI-seq (unfixed HEK nuclei, from McGinnis et al.45) by the distribution of the number of detected transcripts (UMIs; top) and genes (bottom). h. Low correlation between p65 protein (y axis, nCLR) and RELA RNA levels (x axis, log normalized), with Pearson R2 and associated P-value (two-sided t-test). Dots: nuclei colored by treatment (NT, blue; TNFα, red). i. Dynamics of gene expression after LPS stimulation in mouse dendritic cells, from Rabani et al.10, measured across time (x axis). Relative expression to steady state, t0 (y axis): pre-mRNA precursor (blue) and mRNA (red) for total (solid) vs. 4sU labeled (dashed) RNA, shown for Rela (top) and Nfkbia (bottom), a p65 target as in Fig. 1e.

Source data

Extended Data Fig. 2 Flow cytometry of inCITE targets on nuclei or cells extracted from frozen mouse hippocampus.

Flow cytometry of nuclei populations from the mouse hippocampus after intranuclear stains with inCITE antibodies, followed by Alexa Fluor 647-conjugated secondary stain: NeuN in PBS (a), PU.1 in PBS (b), p65 in kainic acid (KA) (d), and c-Fos in PBS (e) and KA (f) treated mice. Axes show fluorescence signal (x axis) and side scatter (y axis) of singlet nuclei (dots); histograms show marginal distributions. Oval gates show NeuNhigh (a, 58.3%), PU.1high (b, <3%), p65high (d, 55.2%), c-Foshigh (0.21% in PBS (e), and 48.7% after KA treatment (f)). c. Right: Distribution of PU.1 in microglia (CD11b+ CX3CR1+, red), CD4+ cells (blue) and isotype (gray) cells measured by flow cytometry (left and middle panels) after simultaneous surface protein and intracellular protein stains (Methods).

Extended Data Fig. 3 Antibody signal varies across concentration regimes.

Antibody stains of the mouse hippocampus (extracted nuclei or in situ) with inCITE antibodies across a wide range of dilutions, targeting NeuN in PBS (a,e), PU.1 in PBS (b,f), p65 in kainic acid (c,g), and c-Fos in kainic acid (d,h) treated mice. Antibody-derived fluorescence measured by Alexa Fluor 647-conjugated secondary antibody stain. a-d. Histograms are normalized as % mode of nuclei singlets. Antibody dilutions are indicated to the right of each axis, with dilutions used for inCITE-seq in bold (NeuN 1:500, PU.1 1:200, p65 1:400, c-Fos 1:400). e-h. In situ immunofluorescence of frozen mouse hippocampus with inCITE antibodies across different dilutions, matching the concentrations used in flow cytometry; representative of 2 independently conducted experiments. Scale bars, 100 µm.

Extended Data Fig. 4 Impact of tissue preparation on epitope detection by antibodies.

Comparing in situ immunofluorescence of antibody stains (followed by Alexa Fluor 647-conjugated secondary stain) in mouse hippocampus tissue that were immediately frozen (green box) or frozen after overnight fixation in 4% PFA (purple box, Methods) across a wide range of antibody dilutions. Images are representative of 2 independent experiments. a. NeuN in PBS. Biolegend NeuN antibody (clone 1B7) used for inCITE and Abcam NeuN antibody (clone EPR12763). b. PU.1 in PBS. Biolegend PU.1 antibody (clone 7C2C34) used for inCITE and Cell Signaling Technology PU.1 antibody (clone 9G7). c. p65 in KA. Biolegend p65 antibody (clone Poly6226) used for inCITE. d. c-Fos in KA treated mice. Biolegend c-Fos antibody (clone Poly6414) used for inCITE and Abcam c-Fos antibody (ab190289). Scale bars, 100 µm.

Extended Data Fig. 5 Comparing and combining single nucleus RNA profiles from inCITE-seq and snRNA-seq of mouse hippocampus.

a. Comparing the complexity of RNA profiles from inCITE-seq and standard snRNA-seq of the mouse hippocampus. Distributions (marginals) of the number of UMIs (x axis) and genes (y axis) from inCITE-seq (left), matching mouse hippocampus snRNA-seq in this study (middle), and previously published snRNA-seq (right). Scatter plot shows the density of individual nuclei (dots) calculated with a Gaussian kernel estimate. b,c. Major cell types from the adult mouse hippocampus identified from inCITE-seq RNA profiles alone. b. UMAP embedding of 24,444 single nucleus inCITE-seq RNA profiles (dots) colored by annotated cluster (number). c. Expression of marker genes (columns) used for annotating cell type clusters (rows), showing mean expression of log normalized counts (dot color) and proportion of expressing cells (dot size). d-j. Enhanced cell type distinctions and annotation by combining RNA profiles from inCITE-seq and snRNA-seq. Joint UMAP embedding of 22,260 inCITE-seq and 15,507 snRNA-seq RNA profiles (dots) colored by unsupervised leiden clusters or subcluster of leiden group 4 (numbers) (Methods). e. Distribution of mitochondrial fraction of total gene content (y axis, left) and total transcript counts (y axis, right) in each leiden cluster or subcluster of leiden group 4 (x axis, both). Asterisks indicate cluster 15 (n = 327 nuclei) and subcluster 4,3 (n = 179 nuclei) that were removed for high mitochondrial content and for low RNA complexity, respectively. f-h. UMAP embedding as in Fig. 2d colored by doublets that were removed from subsequent analyses (n = 3,059 doublets, (f)), batch and assay (g), or condition (h). i. Percent of nuclei (y axis) from each batch/assay (color) in each cluster (x axis). j. Mean expression of log normalized counts (dot color) and proportion of expressing cells (dot size) of marker genes (columns) used for annotating cell type clusters in d (rows).

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Extended Data Fig. 6 Protein levels by inCITE-seq batch (replicate).

a-d. Distribution of protein levels (x axis, nCLR) shown as kernel density estimates of NeuN (a), PU.1 (b), p65 (c), or c-Fos (d) in each batch (top: batch 1; bottom: batch 2) in biologically relevant subsets as foreground (color) and appropriate background set of nuclei (grey). Dashed line: Batch-specific threshold used to partition protein level as high vs. low. e-i. Density distribution of (e) nucleus hashtag counts (x axis, HTOs) or (f-i) antibody-derived tags (x axis, ADTs) of inCITE target proteins, colored by batch (batch 1, gray; batch 2, blue).

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Extended Data Fig. 7 Protein effects on global gene expression.

a. Relation between unspliced pre-mRNA expression of Rbfox3 and nuclear protein levels of NeuN. Distribution of pre-mRNA levels (Z score of log-normalized counts, y axis) in nuclei with high or low levels of NeuN (x axis) after PBS (gray) or KA (green) treatment (NeuN thresholds in Extended Data Fig. 6). Boxplots show the median (centre line), box bounds represent first and third quartiles, and whiskers span from each quartile to the minimum or the maximum (1.5 interquartile range below 25% or above 75% quartiles). Dots correspond to 227 individual nuclei with non-zero pre-mRNA levels measured across n = 2 biologically independent samples. Significance, from left: P = 5*10−15, P = 9*10−5 two-sided Mann-Whitney test. NS – not significant. b. Functional gene sets enriched in TF associated genes. Enrichment (-log10(P-value), x axis, hypergeometric test) of Gene Ontology (GO) terms (y axis) in genes significantly associated (from top to bottom) with p65 (33 genes), PU.1 (13 genes), and c-Fos (10 genes). c. Genes associated with NeuN. Effect size (x axis) and associated significance (y axis, -log10(P-value)) for the association of each gene (dots) with NeuN by a model of gene expression as a linear combination of the four inCITE-seq target proteins after regressing out treatment and cell type (Methods). Select genes are labeled. Colored dots: Benjamini-Hochberg FDR < 5%.

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Extended Data Fig. 8 Genes and modules associated with TFs within excitatory (EX) neurons.

a. Genes associated with protein-protein pairs in the interaction model, identified by modeling gene expression across excitatory neurons as a linear combination of individual proteins and their pairwise interactions after regressing out treatment. Effect size (x axis) and significance (y axis, -log10(P-value)) for DEGs (dots) associated with each protein-protein interaction term: p65 and c-Fos (left), c-Fos and NeuN (middle), and p65 and NeuN (right). Select genes are labeled. Colored dots: Benjamini-Hochberg FDR < 5%. b. Pearson correlation coefficient (red/blue colorbar) of pairwise gene expression profiles (rows and columns) significantly (FDR < 5%) associated positively (purple) or negatively (green), with c-Fos (additive model), p65 (additive model), or c-Fos*p65 (interaction model), ordered by hierarchical clustering. Top bars: Effect size of each protein or protein-protein pair. c. Treatment effect on gene programs. Program scores (y axis) for 5 EX programs (in Fig. 4f) of 15,226 individual nuclei (dots) from PBS or KA treated mice (x axis) measured across 2 biologically independent experiments. Boxplots show the median (centre line), box bounds represent first and third quartiles, and whiskers span from each quartile to the minimum or the maximum (1.5 interquartile range below 25% or above 75% quartiles). Significance, from left: P = 0.049, P = 2.7*10−271, P = 2.2*10−199, P = 6.1*10−7, two-sided Mann-Whitney test. NS – not significant.

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Extended Data Fig. 9 Treatment-dependent cis-regulatory elements and TF-associated genes.

a-c. Prediction of co-regulatory patterns by TF motif enrichment in DEGs associated with c-Fos or p65 (additive model), or their interaction c-Fos*p65 (interaction model). a,b. Significance (-log10(P-value), y axis) and rank order (x axis) of TF motifs (dots) enriched in enhancers of DEGs associated with each protein (additive model) or protein-protein (interaction model) term in excitatory neurons, using enhancers of PBS (a) or KA (b) treated sample as background. Black: significant motifs (P < 10−3, hypergeometric test); gray: not significant. c. TF motif enrichment (columns; dot size, -log10(P-value)) and proportion of excitatory neuron nuclei expressing the RNA (color) of significant TFs (rows) in the enhancers of c-Fos (additive model), p65 (additive model), or c-Fos*p65 (interaction model) DEGs, compared to other enhancers within the KA treated sample. d. Treatment-dependence of gene association with c-Fos and p65. Global effect size of genes (dots) associated with c-Fos (left) and p65 (right), after PBS (x axis) or KA treatment (y axis) (Methods). Colored dots: genes with significant coefficients (Benjamini-Hochberg FDR < 5%) in PBS (gray), KA (green), or both (black). Select genes are labeled. Bottom right: linear correlation R2 and associated P value (two-sided t-test).

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Chung, H., Parkhurst, C.N., Magee, E.M. et al. Joint single-cell measurements of nuclear proteins and RNA in vivo. Nat Methods 18, 1204–1212 (2021). https://doi.org/10.1038/s41592-021-01278-1

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